Papers

Surface energetics of zinc sulfide nanoparticles determines their structure, properties, and occurrence. Using a combination of experimental techniques, we investigated the thermodynamics of the two polymorphs, sphalerite and wurtzite at bulk and nanoscale to understand their occurrence. Calorimetric measurements confirmed that wurtzite has a lower surface energy than sphalerite, which causes a reversal in phase stability at the nanoscale, with wurtzite energetically stable for particle size below 10 nm. Taking these surface energies into account, a simple model of the thermodynamics of the sphalerite - wurtzite transformation as a function of particle size and temperature can explain the occurrence of the zinc sulfide polymorphs in environments as diverse as ore bodies and planetary atmospheres.

Imagine a world where beauty and technology move in perfect harmony, revealing tiny masterpieces hidden in the water. This world is Nature, and diatoms are the stars. Masters of assembling complex hierarchical glass structures, these microscopic algae are true technological gems, and their potential is boundless when it comes to transforming our world. From pharmaceutical applications to biomaterial synthesis, water purification to advanced optical systems, solar cells to the production of new fuels, architecture to design objects, diatoms teach us that Nature's potential is limitless, proving to be invaluable allies for innovation and sustainable development. But how can organisms so small hold such immense potential? In this chapter, we will explore the key characteristics of diatoms and unveil the hidden secrets behind their beauty and multifunctionality, offering an interdisciplinary vision that celebrates the interaction between art and science. We provide concrete examples of structural materials that demonstrate how diatoms can serve as the meeting point between artistic creativity and technological innovation, inspiring new design paradigms and reshaping how we approach materials science and engineering.

The integration of plasma activated water (PAW) with herbal infusions offers a sustainable approach to enhancing the functional bioactivity of plant derived compounds. In this study, neem (Azadirachta indica) and tulsi (Ocimum sanctum) infusions were treated with PAW generated using an atmospheric pressure gliding arc discharge system. The aim was to investigate plasma induced modifications in phytochemicals and their subsequent effects on antimicrobial and antioxidant properties. Spectroscopic (UV Vis, FTIR) and chromatographic (HPLC) analyses demonstrated structural alterations in key polyphenolic constituents, accompanied by mild acidification and changes in redox potential. Total phenolic content (TPC) and flavonoid levels increased significantly following 10 min PAW treatment, while prolonged exposure (15 min) led to partial degradation, suggesting an optimum treatment window. Antioxidant assays (DPPH, ABTS, FRAP) confirmed improved radical scavenging capacity, correlating with enhanced reducing power of modified phytochemicals. Antimicrobial evaluation against Escherichia coli and Staphylococcus aureus revealed synergistic inhibitory effects, with reduced minimum inhibitory concentrations (MIC) for PAW-treated infusions. Collectively, the results highlight the potential of PAW to modulate herbal bioactives, extending their efficacy in natural preservation systems and biomedical formulations. This green plasma-herbal synergy provides a promising pathway toward eco-friendly food safety and healthcare applications.

Wastewater surveillance, which regularly measures pathogen biomarkers in wastewater samples, is a valuable tool for monitoring infectious diseases circulating in communities. Yet, most wastewater-based epidemiology methods that use wastewater surveillance results to infer disease trends implicitly assume that individuals excrete only at their residential locations and that the populations contributing to wastewater samples are static. These simplifying assumptions ignore daily mobility, social interactions, and heterogeneous toilet-use patterns, which can bias the interpretation of wastewater results, especially at upstream sampling locations such as neighborhoods, institutions, or buildings. Here, we introduce an agent-based geospatial simulation framework. Building on an established Patterns of Life model, we simulate daily human activities within a realistic urban environment and extend the framework with a physiologically motivated defecation cycle and toilet-use patterns. We couple this behavioral model with an infectious disease model to simulate transmission through spatial and social interactions. When an infected agent defecates, a pathogen-shedding model determines the amount of pathogen released in the feces. By integrating population mobility, disease transmission, toilet-use behavior, and pathogen shedding, the framework can simulate the spatiotemporal dynamics of wastewater pathogen loads. Using a case study of 10,000 simulated agents in Fulton County, Georgia, we examine how varying infection rates alter epidemic trajectories, wastewater pathogen loads, and the spatial distribution of pathogen shedding over time. Our results show that mobility and toilet use can substantially decouple residential disease prevalence from wastewater pathogen loads and demonstrate how behaviorally grounded simulations can support interpretation, scenario analysis, and wastewater surveillance

Five objections to the conventional arguments underlying the EPR \enquote{paradox} are presented. It is shown that for entangled subsystems the formation of the post-measurement state necessarily involves local interactions affecting both subsystems, contradicting standard EPR assumptions. Correlations between measurements by remote apparatuses are shown to be consistent with relativistic principles. For entangled eigenstates of total momentum or total spin, eliminating redundant degrees of freedom in analogy with generalized Hamiltonian dynamics prevents the emergence of the EPR \enquote{paradox}. A different paradox is identified for a quantum charged particle, whose electric field is shown to be determined by potential configurations encoded in the quantum state rather than by actual measurement events.

This philosophical dialogue explores the idea that the foundational principles of quantum mechanics need not be interpreted as describing a new physics, but may instead arise from the logical necessity of formalising the act of measurement within a coherent algebraic framework. By pushing this perspective to its extreme, the dialogue argues that the core structures of quantum mechanics can be derived independently of any specifically quantum properties of atomic particles, and can be formulated within an otherwise classical theory once limitations of observability and measurement context are taken seriously.

Cross-country soaring flights rely on intermittent atmospheric updrafts to cover long distances, producing trajectories that alternate between rapid relocation and local exploration. From a large dataset of paraglider, hang glider, and sailplane flights, we uncover a universal transport law: beyond short ballistic times, horizontal motion is persistently sub-ballistic, with a Hurst exponent $\approx 0.88$ largely independent of aircraft type. Phase-resolved analysis using a probabilistic segmentation method shows that this scaling arises from the fundamentally intermittent, two-dimensional, and directionally correlated nature of soaring transport, in which successive ballistic segments do not add coherently. We find that learning, in the sense of experience-driven improvements in exploration and decision-making, manifests primarily in the search phase, enhancing the ability to efficiently probe the air mass and locate the next thermal. Overall, our results suggest that atmospheric structure and the generic organization of the transition-search-climb cycle dominate transport properties, placing human soaring alongside biological and physical systems where anomalous transport emerges from intermittency and persistence.

Plasma activated water (PAW) offers a sustainable, nonthermal solution for degrading persistent organic pollutants in industrial effluents. This study employed a gliding arc plasma system to generate PAW for treating diluted waste water containing dyes, pesticides, and pharmaceuticals. Experimental parameters such as exposure time, dilution ratio, and pollutant concentration were varied, with analysis conducted using UV Vis spectroscopy, HPLC, TOC, and COD. Results showed high degradation efficiencies, up to 90% for dyes, 85% for pesticides, and 80% for pharmaceuticals following pseudo first order kinetics driven by hydroxyl and nitrate or nitrite radicals. The findings demonstrate PAWs potential as a green, scalable wastewater treatment strategy that minimizes chemical use, supports water reuse, and enhances environmental safety, with future scope for pilot scale applications.

Primary human cells offer the most faithful representation of native human physiology, yet their practical utility is constrained by the difficulty of introducing exogenous genetic material. Electroporation provides a promising non-viral gene delivery approach; however, conventional bulk systems lack the uniformity and integration required for heterogeneous primary cell samples. Here, we present a vortex-assisted electroporation platform integrating size-selective cell trapping with enhanced throughput, parameter optimization across buffer and electrical conditions, and robust delivery of plasmid DNA and in vitro-transcribed mRNA in primary human cells. This integrated platform provides a unified workflow that addresses sample heterogeneity, throughput demands, and delivery efficiency, enabling broader implementation of non-viral gene delivery into primary cells for research and translational applications.

Variational principles are a unifying mathematical framework across many areas of physics, yet their instruction at the undergraduate level remains primarily analytical. This work presents a pedagogically oriented and computationally enhanced approach to variational modeling that integrates contemporary tools including gradient descent, automatic differentiation, and Physics-Informed Neural Networks (PINNs). Classical variational problems are reformulated as optimization tasks and implemented using open-source Python libraries such as NumPy, Matplotlib, PyTorch, and JAX. The proposed approach is demonstrated through a progression of problems drawn from standard undergraduate curricula, including the derivation of Snell's law from Fermat's principle, projectile motion with and without viscous drag, simple harmonic motion, nonlinear pendulum with damping, steady-state heat conduction governed by the Laplace and Poisson equations with nonlinear temperature-dependent internal heat generation, the double pendulum via the principle of least action, and variational treatments of vibrating strings. In addition, quantum mechanical applications are presented through variational solutions of the hydrogen atom, helium atom, and a schematic nuclear model of the silicon nucleus, illustrating the breadth of the framework across classical, quantum, and nuclear physics. The approach aims to enhance conceptual understanding while simultaneously introducing students to modern computational research methodologies.

We experimentally investigate quasi-two-dimensional (Q2D) forced shallow flows in the presence of solid boundaries and analyze the deviation from the Kolmogorov-Kraichnan (KK) theory. Complex motion is generated using a thin electrolyte subject to electromagnetic forces, and we employ particle tracking velocimetry to resolve the flow properties down to the Kolmogorov scale. Although the velocity probability distribution function closely resembles a Gaussian, deviations from Gaussianity emerge for velocity increments as scales decrease. The second-order structure function supports the onset of local anisotropy at small scales. The sign of the third-order structure function indicates the dominance of the inverse cascade in energy transfer, and the cross-correlation between longitudinal and transverse directions proves to be significant at large scales. The breakdown of local isotropy is consistent with the effect of bottom friction, which primarily affects the longitudinal motion, while leaving the perpendicular direction unaffected. This local anisotropy propagates to larger scales via the inverse energy cascade, with nonlinear interactions eventually influencing the perpendicular direction.

Artificial intelligence has revolutionized optical device design, overcoming the efficiency bottlenecks of traditional methods. For holographic metasurfaces, conventional iterative algorithms suffer from time-consuming iterations and convergence stagnation, especially as the complexity of 3D target fields increases. While recent deep-learning-based algorithms have improved the trade-off between speed and image quality, most existing models remain constrained by predefined physical scenarios (e.g., fixed distances), limiting their adaptability in dynamic practical applications. To address these challenges, we propose a physics-informed neural network (PINN) based on local polynomial fitting and multi-plane wave propagation (LM-PINN) for the rapid design of terahertz 3D holographic metasurfaces. By leveraging a self-supervised training strategy, LM-PINN eliminates the need for labeled datasets, enabling direct end-to-end mapping from target holographic patterns to the metasurface structures. Both simulated and experimental results demonstrate that LM-PINN-designed metasurfaces offer higher imaging quality than traditional iterative algorithms. Crucially, by incorporating a distance encoding process, a single trained LM-PINN generalizes effectively across diverse physical configurations, including varying diffraction distances and distinct 2D or 3D targets, eliminating the necessity for retraining. Furthermore, the inference process of LM-PINN typically takes less than 1 second, providing a multifold speed advantage over traditional algorithms. Consequently, this strategy offers a robust and universal framework that paves the way for high-quality, real-time, and large-scale 3D holographic technologies.

We present the first search for new flavor-dependent long-range interactions (LRI) of neutrinos using publicly available 8 years of high-purity $\nu_\mu$ CC data from IceCube DeepCore. These interactions are mediated by ultra-light gauge bosons with masses below $10^{-10}$ eV, which can arise due to a new lepton-number gauge symmetry, such as $L_e - L_\mu$ or $L_e - L_\tau$. These long-range interactions induce matter potential between neutrinos and abundant electrons present in distant astrophysical sources. These LRI potentials could modify neutrino oscillation probabilities. By probing the effects of LRI on atmospheric neutrino oscillations at IceCube DeepCore, we place world-leading constraints on the coupling strength of these interactions.

Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one strategy is to supplement training protocols for MLIPs with additional learning methods, such as active learning, or fine-tuning. This strategy, however, yields very complex training protocols that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce. To address the above difficulties, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols.

Computing shortest paths for curvature-constrained Dubins vehicles on the unit sphere is fundamental to many engineering applications, including long-range flight planning, persistent surveillance patterns, and global routing problems where great circles are natural routes. Numerical optimization methods on $\SO(3)$ suffer from sensitivity to initialization, may converge to local minima, and often miss feasible solution branches. This paper proposes a unified analytic computational approach for spherical Dubins CGC and CCC paths that overcomes these limitations. By exploiting the axis-fixing property of rotations and developing a closed-form back-substitution method using geometric projection, the three-dimensional boundary value problem is reduced to solving a quadratic polynomial equation. The proposed analytic solver achieves machine precision accuracy with errors on the order of $10^{-16}$, is approximately $717$ times faster than numerical methods under the same computational environment, and systematically enumerates all feasible solution branches without requiring exhaustive multi-start initialization. The method provides closed-form solutions for optimal path computation in the regime where turning radius $\Rturn \in (0, 1/2]$, corresponding to $U_{\max} \geq \sqrt{3}$.

To overcome the limitations of conventional liquid density sensors, MEMS-based approaches have been developed. However, the viscosity-density coupling effect often compromises accuracy in high-viscosity liquids. Although various decoupling strategies have been proposed, they often suffer from complexity and inefficiency. This study presents an AlN-based PMUT liquid density sensor insensitive to viscosity interference. The sensor employs two identical PMUTs, functioning as transmitter and receiver, respectively. An ultrasonic wave generated by the transmitter is reflected by the liquid surface and detected by the receiver. Theoretical calculations demonstrate that when the excitation frequency remains constant, the amplitude of the received electrical signal exhibits a specific relationship with liquid density, while the viscosity-induced signal amplitude variation becomes negligible. Therefore, after calibrating the PMUT liquid density sensor by fitting the relationship between received signal amplitude and density across 0-100% glycerol solutions, the device can measure densities within this range regardless of liquid type and viscosity. Experimental results show that the sensor can accurately measure the density of the propylene glycol solutions using the glycerol-calibrated fitting formula, with the calculated density error rate between measured propylene glycol solutions using the glycerol-calibrated fitting formula and actual densities remains below 0.125%, demonstrating measurement insensitivity to viscosity differences between the two liquids. The proposed method achieves maximum error rates of less than 2.5% in high-viscosity environments (80%-100% glycerol solutions), which is 20% that of other density measurement methods based on resonant frequency. The developed PMUT liquid density sensor exhibits a measurable density range from 0.789 to 1.261 g/cm3.

We investigate induced Compton scattering of a circularly polarized Alfvén wave propagating in a magnetized electron-positron pair plasma using one-dimensional Particle-in-Cell (PIC) simulations. In this system, two distinct modes of density fluctuations, referred to as the charged mode and the neutral mode, are theoretically expected to arise through parametric instabilities. Our simulations confirm these predictions: in the charged mode, the electron and positron densities fluctuate oppositely (Langmuir-like), while in the neutral mode, the charge is Debye-screened and both species fluctuate in phase (acoustic-like). The linear growth rates obtained from the simulations are in good agreement with analytical estimates for both modes. We also find that, in some cases, the linear growth saturates before full scattering occurs, allowing the incident wave to propagate without significant attenuation. Our results allow us to determine whether induced Compton scattering grows linearly in magnetized pair plasmas, offering a foundation for studies of fast radio bursts and laser-plasma experiments.

The n_TOF collaboration has expressed interest in developing transmission experiments with liquid argon (LAr) and gaseous argon (GAr) to improve the understanding of neutron propagation and capture in Ar. This topic is relevant to both nuclear and particle physics, as LAr is widely used in neutrino experiments, dark matter searches and other applications. Despite its widespread use, neutron interactions on Ar are still poorly characterized. In this context, the Multiple Argon Experiments (MArEX) initiative aims to address these gaps by providing detailed measurements of neutron and Ar interactions, including resolved resonance parameters and radiative capture widths, across an unprecedented energy range from 1 eV to 200 MeV. As part of MArEX research activities, this paper presents the development of an innovative High-Pressure Reinforced Vessel (HPRV) for GAr targets, produced by Additive Manufacturing (AM) and reinforced with carbon fibre (CF). The experimental results confirmed the capability of the vessel to withstand high operating pressure without any issues and the capability of the Multi-layers Finite Element Model (MLs-FEM) to simulate and predict the stress behaviour and the operation limit of the vessel.

Rigorous quantum dynamics calculations provide essential insights into complex scattering phenomena across atomic and molecular physics, chemical reaction dynamics, and astrochemistry. However, the application of the gold-standard quantum coupled-channel (CC) method has been fundamentally constrained by a steep cubic scaling of computational cost $[{O}(N^3)]$. Here, we develop a general, rigorous, and robust method for solving the time-independent Schrödinger equation for a single column of the scattering S-matrix with quadratic scaling $[{O}(N^2)]$ in the number of channels. The Weinberg-regularized Iterative Series Expansion (WISE) algorithm resolves the divergence issues affecting iterative techniques by applying a regularization procedure to the kernel of the multichannel Lippmann-Schwinger integral equation. The method also explicitly incorporates closed-channel effects, including those responsible for multichannel Feshbach resonances. We demonstrate the power of this approach by performing rigorous calculations on He + CO and CO + N$_2$ collisions, achieving exact quantum results with quadratic scaling guaranteed by a contour-integral construction. Our results establish a highly scalable computational paradigm, enabling state-to-state quantum scattering computations for complex molecular systems.

Ultra-low-frequency electromagnetic waves Pc1 (0.2-5 Hz), widely known in the literature as pearls, are excited in the outer radiation belt and propagate to the Earth along the geomagnetic field lines in the form of Alfven waves. The study of pearls is of considerable interest for magnetospheric physics. Both spontaneous and stimulated pearl excitation are observed. The paper examines stimulated (trigger) excitation of pearls. A classification of triggers acting on dynamic systems of the magnetosphere is presented. 2 types, 4 classes and 8 species of triggers have been introduced. Examples of triggers of natural and artificial origin are given. The concept of a trigger cascade is introduced. Particular attention is paid to the anthropogenic periodic trigger of pearls. It manifests itself in the form of the so-called Big Ben effect. The essence of the effect is that a series of pearls is often excited immediately following the hour marker according to world time. It is claimed that the connection between the excitation of pearls and hour markers is not accidental, but represents a rather mysterious geophysical phenomenon. It is assumed that the Big Ben effect occurs as a result of the impact on the radiation belt of an unknown type of endogenous periodic artificial trigger. Key words: oscillations and waves, Alfven waves, ion-cyclotron resonator, radiation belt, instability, classification, natural and artificial, technosphere, Big Ben effect.

Mechanosensitive ion nanochannels regulate transport by undergoing conformational changes within nanopores. However, achieving precise control over these conformational states remains a major challenge for both artificial soft or solid pores. Here, we propose an alternative mechanism that modulates the charge carrier density inside nanopores, inspired by transistors in solid-state electronics. This strategy leverages a novel phenomenon of confinement-regulated ion clustering in two-dimensional extremely confined nanochannels, revealed by extensive $\mu$s-scale enhanced-sampling molecular simulations based on an ab initio-refined force field and nucleation theory. The resulting force-ion transistor enables mechanically gated control of ion transport and provides a conceptual foundation for designing ionic mechanical logic gates. Our findings offer new insights into piezochannel mechanosensing and electromechanical coupling in biosystems beyond conformational signaling, opening pathways to integrate artificial ion channels with neuromorphic devices for processing mechanical stimuli.

Grain boundary (GB) migration is a pivotal process that significantly impacts the development of microstructures, thereby influencing the practical performance of polycrystalline materials. Recent advances in 3D experimental techniques have revealed conflicts between observed GB migration behaviors and classical theoretical models. These contradictions raise two fundamental questions, namely, whether GB migration is linearly related to curvature, and how GB energy affect GB migration? Here, we provide a comprehensive analysis of GB migration dynamics in polycrystals and resolve these conflicts within a theoretical framework. Unexpectedly, in a polycrystalline system, the range of GB migration velocities shows little correlation with the magnitude of its curvature. The extent of the influence of GB energy on GB migration is revealed to mostly depend on GB step energy. Finally, a more general GB migration formula is derived to incorporate various driving forces beyond curvature.

Accurate aerodynamic and aerothermodynamic predictions are crucial for numerous hypersonic applications. This paper proposes a gas-kinetic scheme (GKS) coupled with a two-temperature kinetic model, which distinguishes between the translational-rotational and vibrational modes of temperature. Compared with one-temperature model and the translational-rotational multi-temperature model, the proposed model provides a more physically accurate simulation of real gas effects when vibrational energy modes of air are excited. On the other hand, it is computationally simpler than multi-temperature model with independent translational, rotational and vibrational modes. The scheme is implemented on both structured and unstructured grids. To further improve the robustness for strong shock and rarefaction waves, the discontinuity feedback factor is employed instead of traditional limiters. Numerical verifications are conducted on one-dimensional shock structure, two-dimensional (2D) hypersonic flow over a cylinder, 2D hypersonic flow over a wedge and 2D Edney Type IV shock/shock interaction. Compared with experimental data, the reference results from direct simulation Monte Carlo (DSMC) method and Navier–Stokes (NS) solvers, the present method demonstrates accurate prediction of the thermally non-equilibrium shock wave structures and hypersonic flow fields.

Driving non-superconducting materials into a superconducting state through specific modulation is a key focus in the field of superconductivity. Pressure is a powerful method that can switch a three-dimensional (3D) material between non-superconducting and superconducting states. In the two-dimensional (2D) case, strain engineering plays a similar role to pressure. However, purely strain-induced superconductivity in 2D systems remains exceedingly scarce. Using first-principles calculations, we demonstrate that a superconducting transition can be induced solely by applying biaxial tensile strain in a 2D carbon allotrope, THO-graphene, which is composed of triangles, hexagons, and octagons. Free-standing THO-graphene is non-superconducting. Surprisingly, the electron-phonon coupling in strained THO-graphene is enhanced strong enough to pair electrons and realize superconductivity, with the highest superconducting transition temperature reaching 45 K. This work not only provides a notable example of controlling metal-superconductor transition in 2D system just via strain, but also sets a new record of superconducting transition temperature for 2D elemental superconductors.

Traditional cellular force-sensing techniques, such as traction force microscopy (TFM), are predominantly limited to measuring linear tractions, overlooking and technically unable to capture the nanoscale torsional forces that are critical in cell-matrix interactions. Here, we introduce a nanodiamond-enabled torsion microscopy (DTM) that integrates nitrogen-vacancy (NV) centers as orientation markers with micropillar arrays to decouple and quantify nanoscale rotational and translational motions induced by cells. This approach achieves high precision (~1.47 degree rotational accuracy and ~3.13*10-15 Nm torque sensitivity), enabling reconstruction of cellular torsional force fields and twisting energy distributions previously underestimated. Our findings reveal the widespread presence of torsional forces in cell-matrix interactions, introducing "cellular mechanical modes" where different adhesion patterns dictate the balance between traction- and torque- mediated mechanical energy transferred to the substrate. Notably, in immune cells like macrophages that generally exert low linear tractions, torque overwhelmingly dominates traction, highlighting a unique mechanical output for specific cellular functions. By uncovering these differential modes, DTM provides a versatile tool to advance biomechanical investigations, with potential applications in disease diagnostics and therapeutics.

Scanning tunneling microscopy (STM) and micro-electromechanical systems (MEMS) have traditionally addressed vastly different length scales - one resolving atoms, the other engineering macroscopic motion. Here we unite these two fields to perform minimally invasive-measurements of high aspect-ratio MEMS resonators using the STM tip as both actuator and detector. Operating at cryogenic temperatures, we resolve acoustic modes of millimeter-scale, high-Q membranes with picometer spatial precision, without making use of lasers or capacitive coupling. The tunneling junction introduces negligible back-action or heating, enabling direct access to the intrinsic dynamics of microgram-mass oscillators. In this work we explore three different measurement modalities, each offering unique advantages. Combined, they provide a pathway to quantum-level readout and exquisite high-precision measurements of forces, displacements, and pressures at cryogenic conditions. This technique provides a general platform for minimally-perturbative detection across a wide range of nanomechanical and quantum devices.

Tractatus de Conscientia is a tractatus-style sketch toward a modern, physically operational account of consciousness. It is also a tractatus-style attempt to talk about consciousness in a way that stays close to what we can actually test and build. It pushes back against two common moves: treating consciousness as a mysterious extra "stuff," and treating it as nothing more than outward behavior. The central idea is to keep three things separate: what appears for an agent (the lived "given"), what is accessible (what can shape report, control, memory, or other records), and what structure remains when we change descriptions (the invariants of organization). On this view, a conscious episode isn't a mathematical instant. It has a short duration during which many internal distinctions are pulled together into one perspective and held stable enough to guide action–and sometimes to be reported. Unity is captured as a kind of "whole-over-parts" surplus: the system, over a chosen timescale and partition, carries more integrated predictive power than its pieces considered separately, and that surplus must also be available to access channels (so we don't count integration that never makes a difference to anything the agent can do or say). The self, in turn, is treated less like a hidden entity and more like a dynamical role–a self-index that helps bind episodes over time by stabilizing prediction and control across changing contexts. The tractatus also stresses a hard limit: every piece of evidence about consciousness requires coupling to the system, and coupling changes what we observe. So there is no protocol-free, perfectly private "identifier" of what-it-is-like. Consciousness is something we infer and attribute under explicit measurement setups, conventions, and uncertainty bounds–and we should be willing to say "we can't tell" when identifiability runs out.

The NH2 + O reaction represents a critical oxidation pathway in ammonia and hydrazine combustion, yet significant discrepancies persist in reported kinetics. Here, we generate a full-dimensional ground-state potential energy surface (PES) for NH2O using high-level internally contracted multi-reference configuration interaction (ic-MRCI) calculations and the permutation invariant polynomial-neural network (PIP-NN) method. The PES encompasses all energetically accessible channels, including HNO + H, NH + OH, NO + H2, and HON + H. Quasi-classical trajectory calculations on this surface yield thermal rate coefficients and branching ratios over a wide temperature range, particularly extending into the high-temperature regime relevant to combustion. The results provide accurate first principles kinetic data essential for refining combustion models of nitrogen containing fuels.

We demonstrated crystalline silicon-on-sapphire (c-SOS) metasurfaces that convert a Gaussian beam into arrays of complex optical traps, including arrays of optical bottle beams that trap atoms in dark regions interleaved with bright tweezer arrays. The high refractive index and indirect band gap of crystalline silicon makes it possible to design high-resolution near-infrared ($\lambda>700$ nm) metasurfaces that can be manufactured at scale using CMOS-compatible processes. Compared with active components like spatial light modulators (SLMs) that have become widely used to generate trap arrays, metasurfaces provide an indefinitely scalable number of pixels, enabling large arrays of complex traps in a very small form factor, as well as reduced dynamic noise. To design metasurfaces that can generate three-dimensional bottle beams to serve as dark traps, we modified the Gerchberg-Saxton algorithm to enforce complex-amplitude profiles at the focal plane of the metasurface and to optimize the uniformity of the traps across the array. We fabricated and measured c-SOS metasurfaces that convert a Gaussian laser beam into arrays of bright traps, dark traps, and interleaved bright/dark traps.

We study ultrafast magneto-photocurrents in a three-dimensional topological insulator. For this purpose, we excite (In$_r$Bi$_{1-r}$)$_2$Se$_3$ thin films with a femtosecond laser pulse in the presence of an external magnetic field $B_{\text{ext}}$ up to 0.3 T parallel to the film plane. The resulting in-plane photocurrent is measured by detecting the emitted terahertz (THz) electromagnetic pulse. It scales linearly with $B_{\text{ext}}$ and is perpendicular to $B_{\text{ext}}$. Strikingly, for $r\ge$4%, we observe an abrupt photocurrent reduction, which is strongly correlated with the Indium-induced quenching of the topological surface states. The rise time, decay time and amplitude of the THz magneto-photocurrent can consistently be explained by a scenario in which optically excited spin-polarized electrons propagate toward the film surface where the accumulated spin is converted into an in-plane charge current due to spin-velocity locking. Our results are highly relevant for contact-free probing of spin-charge conversion in systems with paramagnetic rather than spontaneous magnetic order.